Condition Monitoring of CNC machines: machining process classification through Temporal Convolutional Networks

The ability of data-driven models to assess the health condition of a CNC machine and its component depends on the operating condition and manufacturing process parameters, i.e., contextual information. Many existing studies focus on predicting fault conditions, assuming to know the contextual information. However, it is rarely acquired and stored, especially in IIoT environments, where machines send unlabeled real-time condition monitoring data to a cloud. Developing lightweight algorithms enables predictive analysis based on condition monitoring data at the edge to extract health and contextual information. This paper exploits this possibility by using a sequence-to-sequence classification approach for classifying different machining processes so that the contextual information can be automatically stored for each manufacturing process sequence. The application of a One-Dimensional Convolutional Neural Network for the sequence-to-sequence classification to a CNC machine demonstrates that (1) condition monitoring data are sufficient to obtain contextual information, and (2) the sequence-to-sequence approach outperforms the feature vector-based classification in terms of training time, training accuracy, and generalization ability.